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14 Slice Timing Correction Temporal interpolation of adjacent time points Usually sinc interpolation Each slice gets a different interpolation Some slices might not have any interpolation Can also be done in the GLM You must know the slice order! X TR1TR2TR3 Slice

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15 Every other slice is bright Interleaved slice acquisition Happens with sequential, harder to see Motion, Slice Timing, Spin History

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16 Motion, Slice Timing, Spin History Slice In TR3 subject moved head up Red is now the first slice Pink is now not in the FoV Red has not seen an RF pulse before (TR=infinite) and so will be very bright Other slices have to wait longer to see an RF pulse, ie, the TR increases slightly, causes brightening. Direction of intensity change depends on a lot of things TR1TR2TR3

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17 B0 Distortion Metric (stretching or compressing) Intensity Dropout A result of a long readout needed to get an entire slice in a single shot. Caused by B0 Inhomogeneity Stretch Dropout

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21 B0 Distortion Correction Can only fix metric distortion Dropout is lost forever Interpolation Need: “Echo spacing” – readout time Phase encode direction More important for surface than for volume Important when combining from different scanners

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22 Spatial Normalization Transform volume into another volume Re-slicing, re-gridding New volume is an “atlas” space Align brains of different subjects so that a given voxel represents the “same” location. Similar to motion correction Preparation for comparing across subjects Volume-based Surface-based Combined Volume-surface-based (CVS)

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27 Spatial Smoothing Replace voxel value with a weighted average of nearby voxels (spatial convolution) Weighting is usually Gaussian 3D (volume) 2D (surface) Do after all interpolation, before computing a standard deviation Similarity to interpolation Improve SNR Improve Intersubject registration Can have a dramatic effect on your results

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32 Choosing the FWHM No hard and fast rules Matched filter theorem – set equal to activation size How big is that? Changes with brain location Changes with contrast May change with subject, population, etc Two voxels – to meet the assumptions of Gaussian Random Fields (GRF) for correction of multiple comparisons

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33 Temporal filtering Replace value at a given time point with a weighted average of its neighbors Should/Is NOT be done as a preprocessing step – contrary to what many people think. Belongs in analysis.